Stage Design Case Study

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Figure 1. Detail of cluster performance across two stages, comparing completion efficiency and production rates between completion designs A and B.

As stage and cluster count in completion design increases, balancing higher cost with improved performance becomes more challenging. Increasing cluster density per stage comes at a cost that may not be warranted indefinitely, since increasing the number of clusters increases the probability of unproductive clusters.

An operator, fresh on the heels of optimizing their treating pressure and proppant concentration, wanted to assess whether increasing their entry points by adding clusters to their current stage length would be economically worth the increased cost and complication. Without increasing stage length, they wanted to evaluate whether 6 or 8 clusters per stage produced more oil.

To test their theory, they completed a well with stages of variable cluster count but fixed stage length and stimulation volumes. They alternated 6 and 8 cluster stages, with variations in order of cluster counts (6, 6, 8, 6, 8, 8, 8) to look for any potential impact from stress shadowing on the stimulation performance.

Because they were interested in cluster level diagnostics, and their cluster spacing was as tight as 20 feet apart, the elected to use Z-System® - deployable fiber optic well intervention – to acquire distributed acoustic (DAS) and temperature data (DTS) across the lateral. This approach allowed them to compare the productivity of the clusters in each stage and identify toe or heel cluster bias, if present.

The Z-System® was dispatched to the well site, rigged up and the spool of carbon-fiber rod was injected into the well while it was producing. Since the 0.6” OD rod does not choke flow, even in production tubing, deferred production was minimized while providing unparalleled acoustic performance compared to more-expensive behind casing fiber. Once deployed across the lateral, DAS and DTS data was collected. Because the rod does not swab or surge the well, the Z-System® is able to capture the most accurate temperature profile, that of undisturbed cluster flow.

The distributed acoustic data was analyzed and a qualitative allocation of production from each cluster was determined, based on the proportional relationship between the flow rate from a cluster and the noise produced. Then, a temperature model, based on the DTS data and constrained by the acoustic analysis, allocated and quantified production per cluster.

Comparative analysis of cluster performance in the 8-cluster and 6-cluster stages indicated that increasing cluster count resulted in an average decrease in cluster efficiency (the ratio of productive clusters to total clusters per stage), at rate of 8% per additional cluster compared to smaller cluster count. At the same time, the total production per stage was on average 16% higher per additional cluster. The operator noted a 33% increase in production, justifying the increased complexity of the higher cluster count, resulting in an optimized completion design for future pad development.

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Figure 2. Subset of stage performance comparing total productivity of stage designs A and B (with alternating designs to check for stress shadowing impact on conclusions)